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Business_Analytics_Presentation_Luke_Caratan

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USINESS
NALYTICS
LUKE CARATAN
DECEMBER 6TH, 2015
BUSINESS ANALYTICS AND
INTELLIGENCE
FALL TRIMESTER 2015 - S...
Data leveraged decision making is not a novel idea. Throughout history, competent
people have been using whatever informat...
The size and richness of these combined data sets makes it
challenging to process. Filtering for temporal relevancy can he...
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  1. 1. 
 Page of1 11 USINESS NALYTICS LUKE CARATAN DECEMBER 6TH, 2015 BUSINESS ANALYTICS AND INTELLIGENCE FALL TRIMESTER 2015 - SESSION B ISTM 660.25FL BOB MCQUAID
  2. 2. Data leveraged decision making is not a novel idea. Throughout history, competent people have been using whatever information is available and relevant in order to make better decisions. Classically speaking, more information means better decisions, and thus the idea of Big Data was born. This ideology held true until very recently when advances in data gathering and storage technologies outpaced developments in processing capabilities. This and other bottlenecks have forced professionals to reevaluate how to interact with data. Josh Wills, Senior Director of Data Science at Cloudera, explains that one or two or even thousands of data points may not be very useful from a business analytics perspective, but that the value in Big Data is only retrievable when the data set is massive(Cloudera). Massive data sets can be expensive to store, difficult to process and, if handled incorrectly, troublesome to learn from. As Data Science professionals constantly evolve their methods of data collection and management, companies get the benefit of richer, more relevant information. Many companies have been collecting structured data for decades. Structured data alone can be directly analyzed, but will only provide one or two dimensional trend analysis. Combining structured and unstructured data creates the opportunity for automated reasoning, and eventually predictive analytics(Andriole). The fastest growing form of big data is unstructured data. Unstructured implies that the the data is not intrinsically quantitative. This type of data can be highly contextual and requires more advanced processing than simple statistical analysis. Almost 80% of newly created data is unstructured(Cloudera). The table below lists some everyday examples of this type of data. Page of2 11 DATA Structured Data Sales History Demographics Quantitative Surveys Financial Data
  3. 3. The size and richness of these combined data sets makes it challenging to process. Filtering for temporal relevancy can help hone in on what really matters within the data. According to a recent report from job listing startup, Textio, “Big Data” may not be the hot topic it once was. Over the past year, job listings have been transitioning to a new term: “Real Time Data”(Bass). In today’s mobile world things happen fast. Social media can take ideas viral in mere minutes. Data scientists realize that the “latest information is more important than having a lot of information(Bass).” As the data sector continues to develop, methods for capturing the right data at the right time will be top of mind. These new practices will provide faster conclusions to more complex business problems. After all, decision makers are not necessarily interested in the data itself, but the secrets trapped inside. 
 Page of3 11 Unstructured Data Social Media: Tweets, blogs, Facebook posts Call Center Notes Emails, Chat History Images, Video Open-ended Surveys Sources: Andriole, Steve. "Unstructured Data: The Other Side of Analytics." Forbes. Forbes Magazine, 5 Mar. 2015. Web. 06 Dec. 2015. Bass, Dina. "Top 10 Rising and Falling Buzzwords in Tech Job Postings.” Bloomberg.com. Bloomberg, 3 Nov. 2015. Web. 06 Dec. 2015. Cloudera: Training A New Generation Of Data Scientists. Dir. Josh Wills. Cloudera. N.p., 3 Sept. 2013. Web. 06 Dec. 2015. Page of3 11
  4. 4. The average person will store information on a computer using the built-in folder —> file system. For typical computer use, this system will fit well and take care of all the needs of the user. However when it comes to business analytics, the requirements are much higher. Data sets are much larger, and can contain many different types of data. The sheer size of the data sets and the diversity of information types calls for a more sophisticated data management system. Businesses need databases. Maxim Levkov summed it up by saying that “databases provide a systematic way to access, process, and correlate data that can be stored for further use.” Databases enable sets of information to be organized and effectively accessed. There are many different types of databases for the many different types of data. Relational databases recognize relationships among stored pieces of information(McNutt). Before the days of big data, this used to be the most common from of database. Its speed and reliability come from its clearly defined structure. Data relationships are built across tables by matching fields within rows. Users interact with the data through Standard Query Language, SQL. Today’s information demands a more dynamic and flexible platform. To overcome the limitations of strictly defined scheme-style data management, “Not-only SQL” (NoSQL) databases have been developed. These databases can handle diverse types of data, and opposed to Relational, NoSQL databases are built to scale(Moniruzzaman). Page of4 11 “Databases provide a systematic way to access, process, and correlate data that can be stored for further use.” -Maxim Levkov, Pepperdine FEMBA DATABASES
  5. 5. The market has developed 5 major classes of NoSQL databases. Each one with its particular strengths and weaknesses outlined in the chart below: Even with advances in database architecture, relational databases have not been completely replaced. In most cases, optimal data management will require some combination of both the old and newer technologies. 
 Main Structure Strengths Weaknesses Examples Column Families columns and groups of columns machine generated/ structured data; limited query capability Apache Hadoop Document stores document rather than data unstructured/semi structured data relatively slower processing Monogo DB; Couch DB; Graph diagram of data relationships web applications; social networking approximations in analytics Neo4J; Horton; Key-Value database key simple and easy development no inherent data relationships Rick; Redis; XML XML nontraditional data; audio/video strict data structure Mark Logic; Sedna; Page of5 11 Sources: Brown, Meta S. "Next-Generation Databases Take On Big Data Management Challenges." Forbes. Forbes Magazine, 30 June 2015. Web. 06 Dec. 2015. Kumar, Girish. "Exploring the Different Types of NoSQL Databases Part Ii." 3Pillar Global. N.p., 07 Oct. 2013. Web. 06 Dec. 2015. McNutt, Louise-Anne. "Relational Database." Encyclopedia of Epidemiology. By Sarah Boslaugh. Los Angeles: Sage Publications, 2008. 908-11. Web. Moniruzzaman, A B M, and Syed Akhter Hossain. "NoSQL Database: New Era of Databases for Big Data Analytics - Classification, Characteristics and Comparison." (n.d.): n. pag. International Journal of Database Theory and Application. Web. 6 Dec. 2015. Sources: Aggregated from Brown, Kumar, and Moniruzzaman Page of5 11
  6. 6. Infrastructure as it relates to business intelligence is another area that is in transition. Historically, infrastructure was maintained with physical hardware on-site and in-house. Small companies would either employ someone to manage IT or outsource the duties. Large companies would have silo’d IT departments that controlled access and protected company data. When the idea of business intelligence began to develop as a management tool, IT departments were expanded and given the responsibility of business analytics. The new trends in infrastructure break through the silos and head to the cloud. Software, Infrastructure, and Platform As A Service offerings allow businesses to access the most up to date technologies at minimal cost. Companies no longer have to deal with building and maintaining their own data centers(Gorelik). Enterprise wide access to information leads to streamlined IT management and the spreading of business analysts throughout the organization. The graphic below outlines a possible workflow from the end user all the way up the chain to the developer(Gilliland). At the top of the chain is the Developer. They are responsible for getting the business Page of6 11 INFRASTRUCTURE
  7. 7. systems working together. Business have data, and they need the proper infrastructure in place to tie the different data sources together. Once the developer makes the data container available, Business Analysts can begin working with reporting tools. This chart lists all examples as “custom” but there are many standard reporting platforms available as well(Baars). Last in the line are the End Users. End users experience the final product of the business intelligence tools. The dashboards and other interactive visualizations deliver insights that pull data from the different business units. This company-wide distillation of information is only possible due to the purposeful integration of the networked data system. Without cloud infrastructure, this rich level of business intelligence would not be possible. Cloud infrastructure is not a requirement, but it does provide many benefits. At a minimum, cloud infrastructure encourages open information across business units within organizations and facilitates the rapid dissemination of business intelligence.
 Page of7 11 Sources: Baars, Henning, and Hans-George Kemper. "Management Support with Structured and Unstructured Data-An Integrated Business Intelligence Framework." Taylor & Francis. Information Systems Management, 7 Apr. 2008. Web. 06 Dec. 2015. Gilliland, Dan. "NetSuite SuiteCloud Platform Overview.” NetSuite. N.p., 28 July 2015. Web. 6 Dec. 2015. Gorelik, Eugene. "Cloud Computing Models." Cloud Computing (2013): n. pag. MIT. Massachusetts Institute of Technology, Jan. 2013. Web. 6 Dec. 2015. Zachman, John A. "Cloud Computing and Enterprise Architecture By: John A. Zachman." Zachman International. N.p., 2011. Web. 06 Dec. 2015. Page of7 11
  8. 8. Analytics are evolving. As computers became commonplace in the corporate world, companies could more easily store business data. The first stage of analytics was explored by companies that collected their own transaction data(Davenport). Some savvy companies would even cross reference their internal data and look for ways to improve efficiencies. This model carried on until computing power grew and eventually data pioneers began looking outside the company for more data feeds. This second stage of analytics quickly grew to include many diverse data types and sources(Davenport). Data sets exploded in size and companies struggled to develop the technologies needed to process and store these incredible amounts of information. Today, agile companies are moving into the 3rd stage of Davenport’s model. Companies are leveraging their data to build better products. During the first wave of innovation, companies have started to integrate multiple types of structured and unstructured data from internal and external sources(Davenport). These intertwining “super sets” can deliver completely new predictive and prescriptive insights. The volume of data generated and recorded is growing at an exponential rate. As companies learn about new data relationships, they should start integrating analytics directly into their business processes(Davenport). These automated events will improve speed of delivery and therefore increase the impact of the derived insights. These data initiatives need support from above if they are to stay on track. Adding the role of Chief Analytics Officer to the C Suite will give the required oversight. On the ground, cross disciplinary teams are key. Seemingly disparate data sources need to understood and conjoined to continue developing successes. Multi-disciplinary skills will be invaluable on these data teams(Davenport). Page of8 11 ANALYTICS
  9. 9. Traditional data warehouses are losing popularity compared to new, more agile data constructs. Companies need capable platforms that facilitate the transfer of data sources in and out of the system. The new age of data is all about prescriptive analytics. Key internal business processes and external customer interactions should have analytics embedded as much as possible. This recipe for advancement will enable companies to make better predictions about customer needs, improve customer service, and eliminate pain points(5 ways). Humans can still think faster than computers can process, but data intensive predictive analytics can help augment human beings to better understand and solve problems faster. 
 Page of9 11 Sources: "5 Ways Companies Are Using Big Data to Help Their Customers." VentureBeat. IDA Singapore, 21 Apr. 2014. Web. 06 Dec. 2015. Davenport, Thomas H. "Analytics 3.0." Harvard Business Review. N.p., Dec. 2013. Web. 06 Dec. 2015. Page of9 11
  10. 10. After gathering, cleaning, and analyzing the data, the final step is to share the findings. Data analytics can reveal powerful insights, but if the findings cannot be communicated, it is a waste of resources. Sharing your findings usually involves written reports or presentations and visual aids are helpful to share the results. Static visualizations are the simplest form of expressing information. Printed maps, charts, and graphs are general examples of standard static data visuals. More advanced interactive visualizations are also becoming popular. Some interactive visuals let users manipulate predefined filters, layers, and queries in order to look at a dataset from a different perspective. In some cases, users are only cycling through different views of pre- processed reports, but a few of the more exciting technologies, Tableau for example, allow visualizations to be processed directly from the actual dataset(Spiegel). Data Visualization can be a effective method of communicating your findings, but only if the preceding steps are taken with care. As an observer of data visualizations, it is important to understand the source of the data. If the visualization is to be credible, then the source of the data must also be credible. Bad, dirty, incomplete, or irrelevant data Page of10 11 VISUALIZATION Getting Started with Data Visualization Geoff McGhee, Stanford University
  11. 11. can undermine the quality of a visualization, and as viewers of the final product, it is difficult to know the quality behind any publication. Crafting meaningful visualizations can be a challenge. Nancy Duarte at The Harvard Business Review offers the following key points to consider: 1. Am I presenting or circulating my data? • Presentation visuals need to be succinct. Use simple lines and contrasting colors to prove the point. Have the back up data tables ready if questioned, but do not include them on the slides. • When circulating information provide more detail. Readers can use as much time as they like to digest the information. 2. Am I using the right kind of chart or table? • Be sure the visualization (chart) projects the relationship you are purporting. 3. What message am I trying to convey? • Use this question to identify and highlight the most important parts of the visualization. 4. Do my visuals accurately reflect the numbers? • Formatting can be fun and pretty, but also distracting. Remember that the value of the visualization is in the point it proves, not how advanced the chart appears. 5. Are my data memorable? • Visualization doesn’t mean “use a chart.” Be sure to use visuals that are striking. The more memorable the visualization, the more effective it will be at communicating the idea. Page of11 11 Sources: Duarte, Nancy. "The Quick and Dirty on Data Visualization." Harvard Business Review. N.p., 16 Apr. 2014. Web. 06 Dec. 2015. McGhee, Geoff. "Getting Started With Data Visualization." Stanford University. The Bill Lane Center for the American West, 5 May 2011. Web. 6 Dec. 2015. Spiegel, Benjamin. "Analytics: A Beginner's Guide To Data Visualization." Marketing Land. N.p., 18 Dec. 2013. Web. 06 Dec. 2015. Page of11 11

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